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Sep 13, 2015
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[MRG+1] DOC Make cv documentation consistent across our codebase #5238

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19 changes: 15 additions & 4 deletions sklearn/calibration.py
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Expand Up @@ -53,10 +53,21 @@ class CalibratedClassifierCV(BaseEstimator, ClassifierMixin):
with too few calibration samples (<<1000) since it tends to overfit.
Use sigmoids (Platt's calibration) in this case.

cv : integer or cross-validation generator or "prefit", optional
If an integer is passed, it is the number of folds (default 3).
Specific cross-validation objects can be passed, see
sklearn.cross_validation module for the list of possible objects.
cv : integer/cross-validation generator/iterable or "prefit", optional
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 3-fold cross-validation,
- integer, to specify the number of folds.
- An object to be used as a cross-validation generator.
- An iterable yielding train/test splits.

For integer/None inputs, if ``y`` is binary or multiclass,
:class:`StratifiedKFold` used. If ``y`` is neither binary nor
multiclass, :class:`KFold` is used.

Refer :ref:`User Guide <cross_validation>` for the various
cross-validation strategies that can be used here.

If "prefit" is passed, it is assumed that base_estimator has been
fitted already and all data is used for calibration.

Expand Down
15 changes: 12 additions & 3 deletions sklearn/covariance/graph_lasso_.py
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Expand Up @@ -463,9 +463,18 @@ class GraphLassoCV(GraphLasso):
The number of times the grid is refined. Not used if explicit
values of alphas are passed.

cv : cross-validation generator, optional
see sklearn.cross_validation module. If None is passed, defaults to
a 3-fold strategy
cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 3-fold cross-validation,
- integer, to specify the number of folds.
- An object to be used as a cross-validation generator.
- An iterable yielding train/test splits.

For integer/None inputs :class:`KFold` is used.

Refer :ref:`User Guide <cross_validation>` for the various
cross-validation strategies that can be used here.

tol: positive float, optional
The tolerance to declare convergence: if the dual gap goes below
Expand Down
84 changes: 56 additions & 28 deletions sklearn/cross_validation.py
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Expand Up @@ -1202,15 +1202,20 @@ def cross_val_predict(estimator, X, y=None, cv=None, n_jobs=1,
The target variable to try to predict in the case of
supervised learning.

cv : integer or cross-validation generator, optional, default=3
A cross-validation generator to use. If int, determines the number
of folds in StratifiedKFold if estimator is a classifier and the
target y is binary or multiclass, or the number of folds in KFold
otherwise.
Specific cross-validation objects can be passed, see
sklearn.cross_validation module for the list of possible objects.
This generator must include all elements in the test set exactly once.
Otherwise, a ValueError is raised.
cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 3-fold cross-validation,
- integer, to specify the number of folds.
- An object to be used as a cross-validation generator.
- An iterable yielding train/test splits.

For integer/None inputs, if ``y`` is binary or multiclass,
:class:`StratifiedKFold` used. If the estimator is a classifier
or if ``y`` is neither binary nor multiclass, :class:`KFold` is used.

Refer :ref:`User Guide <cross_validation>` for the various
cross-validation strategies that can be used here.

n_jobs : integer, optional
The number of CPUs to use to do the computation. -1 means
Expand Down Expand Up @@ -1371,13 +1376,20 @@ def cross_val_score(estimator, X, y=None, scoring=None, cv=None, n_jobs=1,
a scorer callable object / function with signature
``scorer(estimator, X, y)``.

cv : integer or cross-validation generator, optional, default=3
A cross-validation generator to use. If int, determines the number
of folds in StratifiedKFold if estimator is a classifier and the
target y is binary or multiclass, or the number of folds in KFold
otherwise.
Specific cross-validation objects can be passed, see
sklearn.cross_validation module for the list of possible objects.
cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 3-fold cross-validation,
- integer, to specify the number of folds.
- An object to be used as a cross-validation generator.
- An iterable yielding train/test splits.

For integer/None inputs, if ``y`` is binary or multiclass,
:class:`StratifiedKFold` used. If the estimator is a classifier
or if ``y`` is neither binary nor multiclass, :class:`KFold` is used.

Refer :ref:`User Guide <cross_validation>` for the various
cross-validation strategies that can be used here.

n_jobs : integer, optional
The number of CPUs to use to do the computation. -1 means
Expand Down Expand Up @@ -1628,11 +1640,20 @@ def check_cv(cv, X=None, y=None, classifier=False):

Parameters
----------
cv : int, a cv generator instance, or None
The input specifying which cv generator to use. It can be an
integer, in which case it is the number of folds in a KFold,
None, in which case 3 fold is used, or another object, that
will then be used as a cv generator.
cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 3-fold cross-validation,
- integer, to specify the number of folds.
- An object to be used as a cross-validation generator.
- An iterable yielding train/test splits.

For integer/None inputs, if ``y`` is binary or multiclass,
:class:`StratifiedKFold` used. If the estimator is a classifier
or if ``y`` is neither binary nor multiclass, :class:`KFold` is used.

Refer :ref:`User Guide <cross_validation>` for the various
cross-validation strategies that can be used here.

X : array-like
The data the cross-val object will be applied on.
Expand Down Expand Up @@ -1692,13 +1713,20 @@ def permutation_test_score(estimator, X, y, cv=None,
a scorer callable object / function with signature
``scorer(estimator, X, y)``.

cv : integer or cross-validation generator, optional, default=3
A cross-validation generator to use. If int, determines the number
of folds in StratifiedKFold if estimator is a classifier and the
target y is binary or multiclass, or the number of folds in KFold
otherwise.
Specific cross-validation objects can be passed, see
sklearn.cross_validation module for the list of possible objects.
cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 3-fold cross-validation,
- integer, to specify the number of folds.
- An object to be used as a cross-validation generator.
- An iterable yielding train/test splits.

For integer/None inputs, if ``y`` is binary or multiclass,
:class:`StratifiedKFold` used. If the estimator is a classifier
or if ``y`` is neither binary nor multiclass, :class:`KFold` is used.

Refer :ref:`User Guide <cross_validation>` for the various
cross-validation strategies that can be used here.

n_permutations : integer, optional
Number of times to permute ``y``.
Expand Down
19 changes: 14 additions & 5 deletions sklearn/feature_selection/rfe.py
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Expand Up @@ -290,11 +290,20 @@ class RFECV(RFE, MetaEstimatorMixin):
If within (0.0, 1.0), then `step` corresponds to the percentage
(rounded down) of features to remove at each iteration.

cv : int or cross-validation generator, optional (default=None)
If int, it is the number of folds.
If None, 3-fold cross-validation is performed by default.
Specific cross-validation objects can also be passed, see
`sklearn.cross_validation module` for details.
cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 3-fold cross-validation,
- integer, to specify the number of folds.
- An object to be used as a cross-validation generator.
- An iterable yielding train/test splits.

For integer/None inputs, if ``y`` is binary or multiclass,
:class:`StratifiedKFold` used. If the estimator is a classifier
or if ``y`` is neither binary nor multiclass, :class:`KFold` is used.

Refer :ref:`User Guide <cross_validation>` for the various
cross-validation strategies that can be used here.

scoring : string, callable or None, optional, default: None
A string (see model evaluation documentation) or
Expand Down
42 changes: 28 additions & 14 deletions sklearn/grid_search.py
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Expand Up @@ -656,13 +656,20 @@ class GridSearchCV(BaseSearchCV):
the folds, and the loss minimized is the total loss per sample,
and not the mean loss across the folds.

cv : integer or cross-validation generator, default=3
A cross-validation generator to use. If int, determines
the number of folds in StratifiedKFold if estimator is a classifier
and the target y is binary or multiclass, or the number
of folds in KFold otherwise.
Specific cross-validation objects can be passed, see
sklearn.cross_validation module for the list of possible objects.
cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 3-fold cross-validation,
- integer, to specify the number of folds.
- An object to be used as a cross-validation generator.
- An iterable yielding train/test splits.

For integer/None inputs, if ``y`` is binary or multiclass,
:class:`StratifiedKFold` used. If the estimator is a classifier
or if ``y`` is neither binary nor multiclass, :class:`KFold` is used.

Refer :ref:`User Guide <cross_validation>` for the various
cross-validation strategies that can be used here.

refit : boolean, default=True
Refit the best estimator with the entire dataset.
Expand Down Expand Up @@ -850,13 +857,20 @@ class RandomizedSearchCV(BaseSearchCV):
the folds, and the loss minimized is the total loss per sample,
and not the mean loss across the folds.

cv : integer or cross-validation generator, optional
A cross-validation generator to use. If int, determines
the number of folds in StratifiedKFold if estimator is a classifier
and the target y is binary or multiclass, or the number
of folds in KFold otherwise.
Specific cross-validation objects can be passed, see
sklearn.cross_validation module for the list of possible objects.
cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 3-fold cross-validation,
- integer, to specify the number of folds.
- An object to be used as a cross-validation generator.
- An iterable yielding train/test splits.

For integer/None inputs, if ``y`` is binary or multiclass,
:class:`StratifiedKFold` used. If the estimator is a classifier
or if ``y`` is neither binary nor multiclass, :class:`KFold` is used.

Refer :ref:`User Guide <cross_validation>` for the various
cross-validation strategies that can be used here.

refit : boolean, default=True
Refit the best estimator with the entire dataset.
Expand Down
39 changes: 28 additions & 11 deletions sklearn/learning_curve.py
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Expand Up @@ -59,13 +59,20 @@ def learning_curve(estimator, X, y, train_sizes=np.linspace(0.1, 1.0, 5),
be big enough to contain at least one sample from each class.
(default: np.linspace(0.1, 1.0, 5))

cv : integer or cross-validation generator, optional, default=3
A cross-validation generator to use. If int, determines the number
of folds in StratifiedKFold if estimator is a classifier and the
target y is binary or multiclass, or the number of folds in KFold
otherwise.
Specific cross-validation objects can be passed, see
sklearn.cross_validation module for the list of possible objects.
cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 3-fold cross-validation,
- integer, to specify the number of folds.
- An object to be used as a cross-validation generator.
- An iterable yielding train/test splits.

For integer/None inputs, if ``y`` is binary or multiclass,
:class:`StratifiedKFold` used. If the estimator is a classifier
or if ``y`` is neither binary nor multiclass, :class:`KFold` is used.

Refer :ref:`User Guide <cross_validation>` for the various
cross-validation strategies that can be used here.

scoring : string, callable or None, optional, default: None
A string (see model evaluation documentation) or
Expand Down Expand Up @@ -264,10 +271,20 @@ def validation_curve(estimator, X, y, param_name, param_range, cv=None,
param_range : array-like, shape (n_values,)
The values of the parameter that will be evaluated.

cv : integer, cross-validation generator, optional
If an integer is passed, it is the number of folds (defaults to 3).
Specific cross-validation objects can be passed, see
sklearn.cross_validation module for the list of possible objects
cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 3-fold cross-validation,
- integer, to specify the number of folds.
- An object to be used as a cross-validation generator.
- An iterable yielding train/test splits.

For integer/None inputs, if ``y`` is binary or multiclass,
:class:`StratifiedKFold` used. If the estimator is a classifier
or if ``y`` is neither binary nor multiclass, :class:`KFold` is used.

Refer :ref:`User Guide <cross_validation>` for the various
cross-validation strategies that can be used here.

scoring : string, callable or None, optional, default: None
A string (see model evaluation documentation) or
Expand Down
68 changes: 48 additions & 20 deletions sklearn/linear_model/coordinate_descent.py
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Expand Up @@ -1221,11 +1221,18 @@ class LassoCV(LinearModelCV, RegressorMixin):
dual gap for optimality and continues until it is smaller
than ``tol``.

cv : integer or cross-validation generator, optional
If an integer is passed, it is the number of fold (default 3).
Specific cross-validation objects can be passed, see the
:mod:`sklearn.cross_validation` module for the list of possible
objects.
cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 3-fold cross-validation,
- integer, to specify the number of folds.
- An object to be used as a cross-validation generator.
- An iterable yielding train/test splits.

For integer/None inputs, :class:`KFold` is used.

Refer :ref:`User Guide <cross_validation>` for the various
cross-validation strategies that can be used here.

verbose : bool or integer
Amount of verbosity.
Expand Down Expand Up @@ -1360,11 +1367,18 @@ class ElasticNetCV(LinearModelCV, RegressorMixin):
dual gap for optimality and continues until it is smaller
than ``tol``.

cv : integer or cross-validation generator, optional
If an integer is passed, it is the number of fold (default 3).
Specific cross-validation objects can be passed, see the
:mod:`sklearn.cross_validation` module for the list of possible
objects.
cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 3-fold cross-validation,
- integer, to specify the number of folds.
- An object to be used as a cross-validation generator.
- An iterable yielding train/test splits.

For integer/None inputs, :class:`KFold` is used.

Refer :ref:`User Guide <cross_validation>` for the various
cross-validation strategies that can be used here.

verbose : bool or integer
Amount of verbosity.
Expand Down Expand Up @@ -1835,11 +1849,18 @@ class MultiTaskElasticNetCV(LinearModelCV, RegressorMixin):
dual gap for optimality and continues until it is smaller
than ``tol``.

cv : integer or cross-validation generator, optional
If an integer is passed, it is the number of fold (default 3).
Specific cross-validation objects can be passed, see the
:mod:`sklearn.cross_validation` module for the list of possible
objects.
cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 3-fold cross-validation,
- integer, to specify the number of folds.
- An object to be used as a cross-validation generator.
- An iterable yielding train/test splits.

For integer/None inputs, :class:`KFold` is used.

Refer :ref:`User Guide <cross_validation>` for the various
cross-validation strategies that can be used here.

verbose : bool or integer
Amount of verbosity.
Expand Down Expand Up @@ -1985,11 +2006,18 @@ class MultiTaskLassoCV(LinearModelCV, RegressorMixin):
dual gap for optimality and continues until it is smaller
than ``tol``.

cv : integer or cross-validation generator, optional
If an integer is passed, it is the number of fold (default 3).
Specific cross-validation objects can be passed, see the
:mod:`sklearn.cross_validation` module for the list of possible
objects.
cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 3-fold cross-validation,
- integer, to specify the number of folds.
- An object to be used as a cross-validation generator.
- An iterable yielding train/test splits.

For integer/None inputs, :class:`KFold` is used.

Refer :ref:`User Guide <cross_validation>` for the various
cross-validation strategies that can be used here.

verbose : bool or integer
Amount of verbosity.
Expand Down
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